Thermodynamic modeling framework for prediction of tool wear and tool protection phenomena in machining

Bjerke, Axel; Hrechuk, Andrii; Lenrick, Filip; Markström, Andreas; Larsson, Henrik; Norgren, Susanne; M’Saoubi, Rachid; Björk, Thomas; Bushlya, Volodymyr

doi:10.1016/j.wear.2021.203991

Cited by 10 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both of these degradation processes are highly dependent on cutting temperature, and can be slowed by using protective coatings. Similar protection can be achieved if the tool-workpiece elements react in-situ during the cutting process to form a protective reaction layer [23]. Formation of such a layer in machining lead-free brasses was observed earlier by Schultheiss et al and Bushlya et al [6;11].…”

Section: (Iii) Long Tool Lifesupporting

confidence: 56%

On the function of lead (Pb) in machining brass alloys

Johansson

Alm²,

M’Saoubi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Lead has traditionally been added to brass alloys to achieve high machinability, but the exact mechanisms at work are still debated. Lead-free brass alternatives could be developed if these mechanisms were better understood. Accordingly, machinability characteristics were investigated for two brass alloys with similar mechanical properties and phase composition, but with very different machining characteristics because one has 3 wt.% lead (CuZn38Pb3) while the other has only 0.1 wt.% (CuZn42). The effect of the lead was investigated using infrared temperature measurement, electron microscopy, secondary ion mass spectroscopy, quick-stop methods, and high-speed filming. Neither melting of lead nor its deposition on the tool rake surface takes place during machining, thus confirming its limited lubrication and tribological effects. Instead, the main role of lead is to promote discontinuous chip formation. Lead deforms to flake-like shapes that act as crack initiation points when the workpiece material passes through the primary deformation zone. This effect prevents the development of stable tool–chip contact, thus lowering cutting forces, friction, and process temperature.

show abstract

Section: (Iii) Long Tool Lifesupporting

confidence: 56%

On the function of lead (Pb) in machining brass alloys

Johansson

Alm²,

M’Saoubi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such an approach was used in previous work by Vleugels et al in machining steel with ceramic tooling [7][8][9]. Reliable models can aid in predicting wear mechanisms in similar material systems without costly and time-consuming machining trials, sample preparation, and microscopy [10].…”

Section: Introductionmentioning

confidence: 99%

Predicting wear mechanisms of ultra-hard tooling in machining Ti6Al4V by diffusion couples and simulation

Lindvall

Bjerke²,

Salmasi³

et al. 2023

Journal of the European Ceramic Society

Self Cite

View full text Add to dashboard Cite

“…Particularly, comprehension of thermodynamic properties during cutting, and their effects on the cutting tools and workpieces, not only offers guidance on enhancing cutting outcomes but also aids in prolonging the lifespan of tools and reducing production costs [11][12][13][14][15]. Moreover, with the continuous progression of manufacturing technologies, the demand for such intricate studies has only amplified, rendering this line of inquiry pivotal for both technological advancement and economic benefits across the industry.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Numerical Control Tool Cutting Parameters Based on Thermodynamic Response and Machine Learning Algorithms

Zhang

2023

IJHT

View full text Add to dashboard Cite

With the burgeoning demand for high-precision and high-efficiency production, the optimisation of tool cutting parameters has become increasingly paramount. For the first time, a model rooted in thermodynamic response has been proposed, offering a scientific basis for the optimisation of numerical control tool cutting parameters. By conducting an in-depth analysis of the thermal energy produced during the cutting process, this model, through state-of-the-art sensor technology, captures real-time temperature variations in the cutting zone, thereby elucidating thermal flow characteristics under varying cutting parameters. Employing machine learning algorithms, optimal cutting speeds, cutting depths, and feed rates for specific workpiece materials can be predicted and recommended. Preliminary experimental validations indicate that, when compared to conventional optimisation methods, the thermodynamic response-based model significantly enhances cutting efficiency, reduces workpiece thermal deformation, and extends the tool's lifespan. This investigation paves the way for a novel perspective and methodology for the intelligent optimisation of future numerical control tool cutting parameters.

show abstract

Thermodynamic modeling framework for prediction of tool wear and tool protection phenomena in machining

Cited by 10 publications

References 39 publications

On the function of lead (Pb) in machining brass alloys

On the function of lead (Pb) in machining brass alloys

Predicting wear mechanisms of ultra-hard tooling in machining Ti6Al4V by diffusion couples and simulation

Optimisation of Numerical Control Tool Cutting Parameters Based on Thermodynamic Response and Machine Learning Algorithms

Contact Info

Product

Resources

About